Data Cleaning & Descriptives

Matching the bouy data to the other data collected

hilo <- hbb_wku_h_xts
hilo <- data.frame(date=index(hilo), coredata(hilo))
hilo <- hilo[529:54816,]
hilo

# which(hilo$date=="2010-10-23 00:00:00") = index 529 

# which(hilo$date=="2016-12-31 23:00:00") = index 54816

length(hilo[,1]) # we are left with 54288 lines of data

[1] 54288

54816 - length(hilo[,1]) # we lost 528 values 

[1] 528

Changing column names

# removing columns that we are not using 
hilo$date.2 <- NULL # another date column
hilo$date.1 <- NULL # another date column
hilo$BGARFU <- NULL # ?
hilo$cfs <- NULL
hilo$DOmgL <- NULL # dissolved oxygen
#hilo$Doper <- NULL # dissolved oxygen
hilo$PAR1 <- NULL # ?
hilo$pH <- NULL # pH
hilo$NTU <- NULL # a different measurement for turbitity
hilo$DOper10 <- NULL # dissolved oxygen

# colnames(hilo) <- c("Date", "cfs", "RiverFlow-cumec", "LogRiverFlow-cumec", "Chlorophyll-RFU", "Salinity-PPT", "Temp-C", "chlorophyll-calibrator", "Turbidity-NTU")
# does not work ???

hilo

====================================================

FULL DATA SET 2012-2016

Descriptives: PLots

River Flow FULL DATA SET

length(hilo$logcms[which(is.na(hilo$logcms)==TRUE)]) # 12 NAs 

[1] 12

which(is.na(hilo$logcms)==TRUE)

 [1] 50509 50510 50511 50512 50513 50514 50515 50516 50517 50518 50519
[12] 50520

RiverFlow <- ggplot(hilo,  aes(x = date, y = as.numeric(cms))) + 
  geom_line()

print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))

CHL FULL DATA SET

length(hilo$ChlRFU[which(is.na(hilo$ChlRFU)==TRUE)]) # 20464 NAs

[1] 20464

which(as.numeric(hilo$ChlRFU)==max(as.numeric(na.omit(hilo$ChlRFU)))) # 15.3 max 

[1] 38974

# CHL tells us where in the data set this happened  
hilo[38974,]

CHL <- ggplot(hilo,  aes(x = date, y = as.numeric(ChlRFU))) + 
  geom_line()

print(CHL + ggtitle("Chlorophyll ")+labs(x="Time", y = "Chlorophyll  - relative fluorescence units (RFU)"))

Turbity FULL DATA SET

length(hilo$Corr.NTU[which(is.na(hilo$Corr.NTU)==TRUE)]) #15012 NAs

[1] 15012

which(as.numeric(hilo$Corr.NTU)==max(as.numeric(na.omit(hilo$Corr.NTU)))) # 88.4

[1] 33243

# tells us where in the data set this happened
hilo[33243,]

TURB <- ggplot(hilo,aes(x = date, y = as.numeric(Corr.NTU))) + 
  geom_line()

print(TURB + ggtitle("Turbidity ")+labs(x="Time", y = "Turbidity - Nephelometric Turbidity Units (NTU)"))

Salinity FULL DATA SET

length(hilo$saltppt[which(is.na(hilo$saltppt)==TRUE)]) #11330 NAs

[1] 0

SALT <- ggplot(hilo,  aes(x = date, y = as.numeric(saltppt))) + 
  geom_line()

print(SALT + ggtitle("Salinity")+labs(x="Time", y = "Salinity - unit parts per thousand (PPT)"))

Error in FUN(X[[i]], ...) : object 'saltppt' not found

======================================================== # Histograms FULL DATA SET ## River Flow Histogram

CHL Histogram

Turbity Histogram

Salinity Histogram

========================================================= # NEW DATA SET-Modified Data 2013-2015

# start date: 2013-01-01 00:00:00
# end date: 2015-12-31 23:00:00
hilomodified <- hilo[19225:45504,]
length(hilo[,1])-length(hilomodified[,1])

[1] 28008

# lost 28008 entries of data 

length(hilomodified[,1])-528 # we are left with 25752 entries of data 

[1] 25752

head(hilomodified)

tail(hilomodified)

NA

Descriptives on all variables MODIFIED DATA SET: Using Favstats

River Flow Favstats

CHL Favstats

Turbitity Favstats

Salinity Favstats

===================================================== # MODIFIED DATA SET 2013-2015 # Descriptives: Plots

River Flow MODIFIED

CHL MODIFIED

Turbitity MODIFIED

Salinity MODIFIED

Tempurature MODIFIED

Dissolved Oxygen MODIFIED

length(hilomodified$Doper[which(is.na(hilomodified$Doper)==TRUE)]) # 2267 NAs

TempMod <- ggplot(hilomodified,  aes(x = date, y = as.numeric(Doper))) + 
  geom_line()

print(TempMod + ggtitle("Dissolved Oxygen")+labs(x="Time", y = "Dissolved Oxygen in percent of saturation"))

=================================================== # Histograms MODIFIED

River Flow MODIFIED

hist(as.numeric(hilomodified$cms), main = "Histogram of Log River Flow", xlab = "Log River Flow", breaks =90, xlim = c(0,100))

# this looks okay

CHL MODIFIED

Turbitity MODIFIED

Salinity MODIFIED

# skewed
hist(as.numeric(hilomodified$saltppt), main = "Histogram of Salinity", xlab = "Salinity - unit parts per thousand (PPT)")

# this is worst!
hist(log(as.numeric(hilomodified$saltppt)), main = "Histogram of Log Salinity", xlab = "Salinity")
# not sure what happens to units when taking the log 

=============================================================

Plot with ALL Var 2013-2015

It is hard to see what is going on

============================================================== # Descriptives by Storm We are picking one storm from each year. We can indicate a storm has occurred by the extreme events in the river flow data. We will not use the log (which is logbase10) in order to see the extreme events When salinity is below 35 this also indicates a storm has occurred.

We will break the data set by year to find the most extreme event for each year.

2013 Data & Plot

Split 2013 into 6 months to get a better visual

2014 Data & Plot

2015 Data & Plot

# Separating the Data by Storm Events

Separating the Data by Storm

Trying to make the Rainfall plot easier to read

RiverFlow1 <- ggplot(hilomodified[1:100,],  aes(x = date, y = as.numeric(cms))) + 
  geom_line()

print(RiverFlow + ggtitle("River Flow")+labs(x="Time", y = "River Flow - cubic meters per second"))

=======

NEW Change in River Flow Column

length(end)

[1] 72

for(i in 1:length(start)){
  storm <- ggplot(hilomodified[(start[i]-24):(end[i]+24),],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm + ggtitle("Storm 1/7/13")+labs(x="Time"))
}

ChangeRF <- function(x = vector()){
change <- c(0)
  for(i in 1:(length(x)-1)){
    change[i+1] <- x[i]-x[i+1]
  }
  return(change)
}
change.vector <- c(ChangeRF(as.numeric(hilomodified$cms)))
change.vector

   [1]   0.00000   0.02124   0.02832   0.02124   0.02832
   [6]   0.00708   0.00000   0.02124   0.02124   0.02832
  [11]   0.01416   0.00000   0.00708   0.00708   0.01416
  [16]   0.02124   0.00708   0.00708   0.01416   0.03540
  [21]   0.00000   0.01416   0.00000   0.02832   0.02124
  [26]   0.02124   0.00000   0.01416   0.01416   0.00000
  [31]   0.00000   0.00000   0.02832  -0.00708   0.02124
  [36]   0.00000   0.01416   0.02124   0.00000   0.02124
  [41]   0.01416   0.00000   0.00000   0.00708   0.00708
  [46]   0.00708   0.02832   0.00708   0.00708   0.01416
  [51]   0.00000   0.00708   0.00000   0.00000   0.02124
  [56]   0.00000   0.01416   0.02124  -0.01416   0.01416
  [61]   0.00000   0.00000   0.00708   0.02124   0.00708
  [66]   0.00708   0.01416   0.00000   0.00000   0.00708
  [71]   0.02832   0.00708   0.00708   0.00708   0.01416
  [76]   0.00000   0.00000  -0.01416   0.01416   0.01416
  [81]   0.00000   0.00000   0.00000   0.00000   0.00000
  [86]  -0.00708  -0.02832  -0.04248  -0.00708   0.00000
  [91]  -0.02124  -0.02832  -0.04248  -0.02124  -0.01416
  [96]  -0.02124  -0.02124   0.01416   0.03540   0.00708
 [101]   0.00000   0.00708   0.00708  -0.19116  -0.25488
 [106]  -0.29028  -0.16284   0.09204  -0.33984  -0.44604
 [111]   0.04956   0.16284   0.12036  -0.11328  -0.22656
 [116]  -0.27612  -0.31152  -1.13988  -1.58592   0.59472
 [121]   0.67968   0.49560   0.14160   0.00708   0.21948
 [126]   0.04248  -0.41064  -0.95580  -0.45312   0.32568
 [131]   0.53100   0.54516   0.56640   0.32568   0.29736
 [136]   0.06372  -0.45312  -0.82836  -0.63720  -1.22484
 [141]  -1.92576  -2.30100  -2.73996  -1.72044  -0.54516
 [146]  -0.25488  -0.09912  -0.43896  -1.32396  -1.54344
 [151]  -1.43724  -1.09032  -0.55224  -0.10620   0.44604
 [156]   0.92748   1.83372   2.47092   2.03196   1.81248
 [161]   1.60008   1.35228   1.06908   0.86376   0.75756
 [166]   0.66552   0.54516   0.43188   0.43896   0.31860
 [171]   0.26196   0.24780   0.19116   0.16992   0.09912
 [176]   0.13452   0.04956   0.09912   0.02124   0.04956
 [181]   0.02832   0.03540   0.00000   0.05664   0.02124
 [186]   0.02832   0.02832   0.04248  -0.00708   0.00708
 [191]  -0.07788  -0.12036  -0.04956  -0.16284  -0.12036
 [196]  -0.21240  -0.20532  -0.25488  -0.80004   0.01416
 [201]   0.05664  -0.24780  -0.41064   0.06372   0.24780
 [206]   0.27612   0.22656   0.24780   0.12036   0.02832
 [211]  -0.35400  -1.84080  -5.09760  -6.35784 -10.52088
 [216]  -4.71528   3.39840   4.29756   3.27096   2.61252
 [221]   2.20188   1.98948   1.67088   1.46556   1.35228
 [226]   1.20360   1.00536   0.80004   0.75048   0.56640
 [231]   0.51684   0.40356   0.40356   0.34692   0.24780
 [236]   0.02124   0.00708   0.14868   0.10620   0.19116
 [241]   0.14160   0.12036   0.11328   0.09204   0.05664
 [246]   0.03540   0.05664   0.02124   0.06372   0.07080
 [251]   0.02124   0.02832   0.04956   0.03540   0.04956
 [256]   0.02124   0.01416   0.04956   0.04248   0.04248
 [261]   0.02124   0.04956   0.01416   0.03540   0.02832
 [266]   0.04248   0.03540   0.02124   0.04956   0.03540
 [271]   0.00708   0.05664   0.04248   0.02832   0.03540
 [276]   0.03540   0.04248   0.02832   0.02124   0.02832
 [281]   0.03540   0.03540   0.03540   0.02124   0.05664
 [286]   0.01416  -0.04956  -0.18408   0.14160   0.07788
 [291]  -0.04956  -0.09912  -0.19824  -0.23364  -0.36108
 [296]  -0.16284   0.06372   0.14868   0.19116   0.18408
 [301]   0.14868   0.11328   0.08496   0.05664   0.07080
 [306]   0.04248   0.02832   0.02832   0.02832   0.05664
 [311]   0.02832   0.00708   0.04248   0.00708   0.02124
 [316]   0.02832   0.02832   0.02832   0.01416   0.02832
 [321]   0.01416   0.01416   0.01416   0.02832   0.01416
 [326]   0.02124   0.01416   0.00708   0.00000   0.03540
 [331]   0.01416   0.02124   0.02124   0.01416   0.00708
 [336]   0.01416   0.00708   0.02832   0.01416   0.02124
 [341]   0.00000   0.00708   0.00000   0.02832   0.01416
 [346]   0.02124  -0.00708   0.02832  -0.00708   0.02124
 [351]   0.00708   0.00000   0.00000   0.02124   0.00708
 [356]   0.00708   0.01416   0.00708   0.00708   0.02124
 [361]   0.00708   0.03540   0.00000   0.00708   0.02124
 [366]   0.01416   0.00000   0.02124   0.01416   0.00708
 [371]   0.01416   0.00000   0.02832  -0.02124   0.01416
 [376]   0.00708  -0.01416   0.00708   0.02124   0.00000
 [381]   0.01416   0.00000   0.00708  -0.00708   0.02124
 [386]   0.01416   0.02124   0.00708   0.00708   0.01416
 [391]   0.00000   0.02124   0.00000   0.00708   0.00000
 [396]   0.02124  -0.01416   0.02124   0.00708   0.01416
 [401]   0.00708   0.01416   0.00000   0.01416   0.00000
 [406]   0.00000   0.02124   0.00708   0.00000   0.00708
 [411]   0.00708   0.01416   0.00000   0.00000   0.00000
 [416]   0.02124  -0.01416   0.02832   0.00000   0.01416
 [421]   0.00708   0.00000   0.00000   0.00000   0.00000
 [426]   0.00708   0.01416   0.01416   0.00708   0.01416
 [431]   0.01416   0.00708  -0.00708   0.01416   0.00000
 [436]   0.00000   0.00708   0.01416  -0.07080  -0.56640
 [441]   0.01416   0.21948   0.16284   0.10620   0.05664
 [446]   0.04956   0.02124   0.01416   0.01416   0.02124
 [451]   0.01416   0.00708   0.00000   0.00708   0.00708
 [456]   0.01416   0.02124   0.00000   0.00708   0.00000
 [461]   0.00000   0.00000   0.00000   0.01416   0.00708
 [466]   0.00708   0.00000   0.00000   0.00000   0.00000
 [471]   0.00000   0.00708   0.01416   0.00708   0.00708
 [476]   0.00708   0.01416   0.00000   0.00000   0.00000
 [481]   0.00000   0.00708   0.00708   0.01416   0.00000
 [486]   0.00000   0.00000   0.00000   0.00000   0.01416
 [491]   0.00000   0.01416   0.00000   0.00000   0.01416
 [496]   0.00708   0.00708   0.00000   0.00000   0.00000
 [501]   0.00000   0.00000   0.01416   0.00708   0.00708
 [506]   0.00000   0.00000   0.00000   0.00000   0.00000
 [511]   0.00000   0.00000   0.00708   0.01416   0.00000
 [516]   0.00000   0.00708   0.00000   0.00000   0.00000
 [521]   0.00000   0.00000   0.01416   0.01416   0.00000
 [526]   0.00000   0.00000   0.00000   0.00000   0.00000
 [531]   0.00000   0.00708   0.00000   0.00708   0.00708
 [536]   0.00000   0.00000   0.00708   0.00000   0.00000
 [541]   0.00000   0.00000  -0.00708   0.00708   0.00000
 [546]   0.00000   0.00000   0.00000   0.01416   0.01416
 [551]  -0.01416   0.00708   0.00000  -0.01416   0.00708
 [556]   0.00000  -0.01416   0.00000  -0.02124  -0.00708
 [561]   0.00000   0.00000   0.00000   0.00000   0.00000
 [566]   0.00000   0.00000   0.02832   0.00000   0.00000
 [571]   0.02124   0.00708   0.00000   0.00708  -0.00708
 [576]   0.00000   0.01416  -0.00708  -0.01416  -0.02124
 [581]  -0.00708  -0.01416  -0.00708   0.00000  -0.02124
 [586]  -0.00708   0.00000   0.00000   0.00000   0.00708
 [591]   0.01416   0.00708   0.01416   0.01416   0.00708
 [596]   0.02124   0.00000   0.00000   0.00708   0.02124
 [601]   0.00000   0.00000   0.00708   0.00708   0.01416
 [606]   0.00000   0.00000   0.00000   0.00000   0.00000
 [611]   0.00000   0.01416   0.01416   0.00000   0.00000
 [616]   0.00000   0.00000   0.00000   0.00000   0.00708
 [621]   0.00708   0.01416   0.00000   0.00000   0.00000
 [626]   0.00000   0.00000   0.00000   0.00000   0.00000
 [631]   0.00000   0.00000   0.00000   0.00000   0.00708
 [636]  -0.00708   0.02832  -0.00708   0.00708   0.00000
 [641]   0.00000   0.00000   0.00000   0.00000   0.00708
 [646]  -0.00708   0.00000   0.00708   0.00708   0.01416
 [651]  -0.00708   0.00708   0.00000   0.00000   0.00000
 [656]   0.00000  -0.07080  -0.14868   0.00000   0.04248
 [661]   0.04956   0.04248   0.02832   0.02124   0.00708
 [666]   0.02124   0.00708   0.00000   0.00000   0.01416
 [671]   0.00708   0.00708   0.00000   0.00000   0.00000
 [676]   0.00000   0.00000   0.00000   0.00000   0.00000
 [681]   0.00000   0.00000   0.00000   0.00000   0.00000
 [686]   0.00000   0.00000   0.00000  -0.04956  -0.08496
 [691]  -0.33984  -0.17700   0.19824   0.13452   0.06372
 [696]   0.02832   0.02832   0.02832   0.02832   0.02124
 [701]   0.02124   0.01416   0.02124   0.00708   0.00000
 [706]   0.00708   0.02124   0.00000   0.00000   0.00708
 [711]   0.00708   0.01416  -0.00708  -0.01416   0.00708
 [716]   0.01416   0.00000   0.00000   0.00000   0.00000
 [721]   0.00000   0.00000   0.00000   0.01416   0.00708
 [726]   0.00000   0.00708   0.00000  -0.02124   0.00708
 [731]   0.01416   0.00000   0.00000   0.00000   0.00000
 [736]   0.00000   0.00000   0.00000   0.00000   0.00000
 [741]   0.02124   0.00708   0.00000   0.00000   0.00000
 [746]   0.00000  -0.02124  -0.02124  -0.06372  -0.08496
 [751]  -0.10620  -0.14868  -0.46020  -0.33984  -0.02124
 [756]   0.09912   0.19116   0.15576   0.12744   0.09912
 [761]   0.07788   0.07788   0.05664   0.03540   0.03540
 [766]   0.02832   0.02832   0.02124   0.02124   0.01416
 [771]   0.02124   0.00708   0.02832   0.00000   0.00708
 [776]   0.02124   0.00000   0.00000   0.00708   0.02124
 [781]   0.00000   0.00000   0.00000   0.02124   0.00708
 [786]   0.00000   0.00000   0.00000   0.00000   0.00708
 [791]   0.01416   0.00708   0.00000   0.00000   0.00000
 [796]   0.00000   0.00000   0.00000   0.00000   0.00000
 [801]   0.00000   0.00708   0.00000   0.02124  -0.01416
 [806]   0.00708   0.00708   0.00000   0.00000   0.00000
 [811]   0.00000   0.00000   0.00000   0.00000   0.00708
 [816]   0.00000  -0.02124  -0.08496  -0.04248   0.00000
 [821]   0.00000   0.00000   0.00000   0.00000   0.00000
 [826]   0.00000   0.00000   0.00000   0.01416   0.00000
 [831]  -0.00708   0.02124  -0.02124  -0.03540   0.02832
 [836]   0.02832   0.00000   0.00708  -0.00708   0.02124
 [841]  -0.01416  -0.00708   0.00000  -0.01416  -0.04956
 [846]  -0.02124  -0.02832  -0.03540  -0.03540  -0.04956
 [851]  -0.12036  -0.07080  -0.14160  -0.09912  -0.01416
 [856]   0.04956   0.04956   0.06372   0.05664   0.00000
 [861]   0.00708   0.00000   0.00708   0.01416   0.00708
 [866]  -0.00708   0.00000   0.00708   0.00000   0.00000
 [871]   0.00000   0.00708   0.01416   0.02124   0.01416
 [876]   0.01416   0.01416   0.00708   0.02124   0.00000
 [881]   0.00708  -0.00708  -0.04956  -0.03540  -0.47436
 [886]  -2.93112  -4.42500   1.09032   0.65844   0.18408
 [891]  -1.51512  -5.38788 -10.78992 -15.70344  -2.61960
 [896]   6.15960   6.93840   4.41792   4.63032   3.46920
 [901]   2.68332   1.99656   1.49388   1.43724   2.80368
 [906]  -0.43896   0.39648   0.50976   0.41772   0.29736
 [911]   0.30444   0.23364   0.16992   0.03540   0.11328
 [916]   0.17700   0.13452   0.16284   0.16992   0.07080
 [921]   0.09204   0.12744   0.07080   0.08496   0.09912
 [926]   0.10620   0.07788   0.07788   0.07080   0.07788
 [931]   0.07788   0.09912   0.07080   0.04248   0.07080
 [936]   0.08496   0.06372   0.05664   0.04956   0.04956
 [941]   0.04248   0.04956   0.04248   0.04956   0.02832
 [946]   0.04956   0.01416   0.02832   0.04248   0.03540
 [951]  -0.02832  -0.00708   0.01416  -0.04248  -0.22656
 [956]  -0.43188  -0.77880  -0.53100  -0.30444  -0.81420
 [961]  -1.50096  -1.16820  -0.21240  -0.11328   0.22656
 [966]   0.46020   0.49560   0.46728   0.47436   0.41064
 [971]   0.58764   0.67968   0.36108   0.23364   0.15576
 [976]   0.13452   0.07788   0.12744   0.07788   0.12036
 [981]   0.08496   0.06372   0.07080   0.10620   0.02124
 [986]  -0.00708   0.08496   0.07080   0.03540   0.05664
 [991]   0.02124   0.06372   0.05664   0.04956   0.04248
 [996]   0.03540   0.03540   0.04956   0.02124   0.04248
 [ reached getOption("max.print") -- omitted 25280 entries ]

length(ChangeRF(as.numeric(hilomodified$cms)))

[1] 26280

length(hilomodified$cms) # same length

[1] 26280

hilomodified$changeCMS <- change.vector

head(hilomodified)

# 10-11 negative start of storm
# 11-10 positive end of storm

positive.change <- vector()
negative.change <- vector()
index <- vector()


for(i in 1:length(hilomodified$cms)){
  if (hilomodified$changeCMS[i] < 0 ){
    negative.change[i] <- i
  }else{
    if(hilomodified$changeCMS[i] > 0){
      postive.change[i] <- i
    }
  }
  if(hilomodified$cms[i] >= 10 ){
    index[i] <- i
  }
}
index
positive.change
negative.change
length(which(index > 0)) 

storms <- sort(c(positive.change,negative.change), decreasing = FALSE)
storms
hilostorms <- hilomodified[storms,]
hilostorms

storm <- ggplot(hilostorms[50:250,],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm + ggtitle("Storm 1/7/13")+labs(x="Time"))

vector <- vector()
for(i in 1:length(storms)){
  if(isTRUE(abs(storms[i]-storms[i+1]) > 10)){
    stop <- storms[i]
  }else{
    stop <- 0
  }
  if(stop > 0){
    vector[i] <- stop
  }
}
vector[which(is.na(vector)==FALSE)]

  [1]   622   672  1706  1898  1924  1970  2081  2327  2388
 [10]  2429  2469  2493  2515  2537  2576  2714  2734  2772
 [19]  2799  2825  2866  2947  2998  3051  3068  3130  3196
 [28]  3211  3226  3249  3373  3746  3841  3856  3898  4343
 [37]  4412  4438  4460  4507  4534  4617  4660  4827  5006
 [46]  5087  5107  5135  5237  5667  5690  5735  5767  5786
 [55]  5837  5851  5888  5900  5921  5968  6048  6068  6092
 [64]  6240  6270  6284  6320  6363  6379  6456  6488  6599
 [73]  6630  6864  6888  6908  6945  6959  6977  6999  7025
 [82]  7063  7076  7099  7134  7150  7222  7308  7443  7466
 [91]  7493  7678  7703  7733  7786  7805  7840  7863  7882
[100]  7933  7960  7978  8005  8031  8124  8167  8954  9013
[109]  9067  9091  9135  9160  9188  9256  9280  9303  9333
[118]  9377  9487  9505 10205 12119 12216 12238 13083 13095
[127] 14759 14786 14812 14831 14891 14946 14998 15028 15079
[136] 15099 15125 15140 15165 15187 15209 15526 15540 15553
[145] 15702 17365 17482 17512 17533 17585 17612 17657 17708
[154] 17734 17778 17811 17838 17871 17925 17978 18210 18233
[163] 18358 18888 18905 18921 19117 19192 19225 19269 19322
[172] 19342 19362 19378 19490 19504 19522 19628 21792 21813
[181] 21948 21981 22016 22075 22124 22220 23568

storm.1.7.13 <- ggplot(hilomodified[1:80,],  aes(x = date, y = as.numeric(cms))) +
  geom_line(color="black")+
  geom_line(aes(y = as.numeric(Corr.NTU)), color="grey69") +
  geom_line(aes(y=as.numeric(ChlRFU)),color="khaki")

print(storm.1.7.13 + ggtitle("Storm 1/7/13")+labs(x="Time"))